Search results for: usual stochastic order

Authors: Vishal Jaunky, Jonas Grafström

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The development of the European Union’s (EU) single economic market and rapid technological change has resulted in major structural changes in EU’s member states economies. The general liberalization process that the countries has undergone together has convinced the governments of the member states of need to upgrade their economic and training systems in order to be able to face the economic globalization. Several signs of economic convergence have been found but less is known about the knowledge production. This paper addresses the convergence pattern of technological innovation in 13 European Union (EU) states over the time period 1990-2011 by means of parametric and non-parametric techniques. Parametric approaches revolve around the neoclassical convergence theories. This paper reveals divergence of both the β and σ types. Further, we found evidence of stochastic divergence and non-parametric convergence approach such as distribution dynamics shows a tendency towards divergence. This result is supported with the occurrence of γ-divergence. The policies of the EU to reduce technological gap among its member states seem to be missing its target, something that can have negative long run consequences for the market.

Keywords: convergence, patents, panel data, European union

Procedia PDF Downloads 264

Authors: Koushlendra Kumar Singh, Manish Kumar Bajpai, Rajesh Kumar Pandey

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A discrete time fractional orderdifferentiator has been modeled for estimating the fractional order derivatives of contaminated signal. The proposed approach is based on Chebyshev’s polynomials. We use the Riemann-Liouville fractional order derivative definition for designing the fractional order SG differentiator. In first step we calculate the window weight corresponding to the required fractional order. Then signal is convoluted with this calculated window’s weight for finding the fractional order derivatives of signals. Several signals are considered for evaluating the accuracy of the proposed method.

Keywords: fractional order derivative, chebyshev polynomials, signals, S-G differentiator

Procedia PDF Downloads 626

Authors: Jin-Qu Chen, Jie Liu, Yong Yin, Zi-Qi Ju, Yu-Yao Wu

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Increase in travel fare and station’s failure will have huge impact on passengers’ travel. The Urban Rail Transit (URT) station’s accessibility reliability under increasing travel fare and station failure are analyzed in this paper. Firstly, the passenger’s travel path is resumed based on stochastic user equilibrium and Automatic Fare Collection (AFC) data. Secondly, calculating station’s importance by combining LeaderRank algorithm and Ratio of Station Affected Passenger Volume (RSAPV), and then the station’s accessibility evaluation indicators are proposed based on the analysis of passenger’s travel characteristic. Thirdly, station’s accessibility under different scenarios are measured and rate of accessibility change is proposed as station’s accessibility reliability indicator. Finally, the accessibility of Hangzhou metro stations is analyzed by the formulated models. The result shows that Jinjiang station and Liangzhu station are the most important and convenient station in the Hangzhou metro, respectively. Station failure and increase in travel fare and station failure have huge impact on station’s accessibility, except for increase in travel fare. Stations in Hangzhou metro Line 1 have relatively worse accessibility reliability and Fengqi Road station’s accessibility reliability is weakest. For Hangzhou metro operational department, constructing new metro line around Line 1 and protecting Line 1’s station preferentially can effective improve the accessibility reliability of Hangzhou metro.

Keywords: automatic fare collection data, AFC, station’s accessibility reliability, stochastic user equilibrium, urban rail transit, URT

Procedia PDF Downloads 114

Authors: N. R. Badurally Adam, S. R. Monebhurrun, M. Z. Dauhoo, A. Khoodaruth

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Transport energy demand is vital for the economic growth of any country. Globalisation and better standard of living plays an important role in transport energy demand. Recently, transport energy demand in Mauritius has increased significantly, thus leading to an abuse of natural resources and thereby contributing to global warming. Forecasting the transport energy demand is therefore important for controlling and managing the demand. In this paper, we develop a model to predict the transport energy demand. The model developed is based on a system of five stochastic differential equations (SDEs) consisting of five endogenous variables: fuel price, population, gross domestic product (GDP), number of vehicles and transport energy demand and three exogenous parameters: crude birth rate, crude death rate and labour force. An interval of seven years is used to avoid any falsification of result since Mauritius is a developing country. Data available for Mauritius from year 2003 up to 2009 are used to obtain the values of design variables by applying genetic algorithm. The model is verified and validated for 2010 to 2012 by substituting the values of coefficients obtained by GA in the model and using particle swarm optimisation (PSO) to predict the values of the exogenous parameters. This model will help to control the transport energy demand in Mauritius which will in turn foster Mauritius towards a pollution-free country and decrease our dependence on fossil fuels.

Keywords: genetic algorithm, modeling, particle swarm optimization, stochastic differential equations, transport energy demand

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Authors: Jacob Medupe

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Life as we know it has changed since the global outbreak of Coronavirus Disease 2019 (COVID-19) and business as usual will not continue. The human impact of the COVID-19 crisis is already immeasurable. Moreover, COVID-19 has already negatively impacted economies, livelihoods and disrupted food systems around the world. The disruptive nature of the Corona virus has affected every sphere of life including the culture and teaching and learning. Right now the majority of education research is based around classroom management techniques that are no longer necessary with digital delivery. Instead there is a great need for new data about how to make the best use of the one-on-one attention that is now becoming possible (Diamandis & Kotler, 2014). The COVID-19 pandemic has necessitated an environment where the South African learners are focused to adhere to social distancing in order to minimise the wild spread of the Corona virus. This arrangement forces the student to utilise the online classroom technologies to continue with the lessons. The historical reality is that the country has not made much strides on the closing of the digital divide and this is particularly a common status quo in the deep rural areas. This will prove to be a toll order for most of the learners affected by the Corona Virus to be able to have a seamless access to the online learning facilities. The paper will seek to look deeply into this reality and how the Corona virus has brought us to the reality that South Africa remains a deeply unequal society in every sphere of life. The study will also explore the state of readiness for education system around the online classroom environment.

Keywords: virtual learning, virtual classroom, COVID-19, Corona virus, internet connectivity, blended learning, online learning, distance education, e-learning, self-regulated Learning, pedagogy, digital literacy

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Authors: Amer Ait Sidhoum

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Interest in sustainability has gained ground among practitioners, academics and policy-makers due to growing stakeholders’ awareness of environmental and social concerns. This is particularly true for agriculture. However, relatively little research has been conducted on the quantification of social sustainability and the contribution of social issues to the agricultural production efficiency. This research's main objective is to propose a method for evaluating prices of social outputs, more precisely shadow prices, by allowing for the stochastic nature of agricultural production that is to say for production uncertainty. In this article, the assessment of social outputs’ shadow prices is conducted within the methodological framework of nonparametric Data Envelopment Analysis (DEA). An output-oriented directional distance function (DDF) is implemented to represent the technology of a sample of Catalan arable crop farms and derive the efficiency scores the overall production technology of our sample is assumed to be the intersection of two different sub-technologies. The first sub-technology models the production of random desirable agricultural outputs, while the second sub-technology reflects the social outcomes from agricultural activities. Once a nonparametric production technology has been represented, the DDF primal approach can be used for efficiency measurement, while shadow prices are drawn from the dual representation of the DDF. Computing shadow prices is a method to assign an economic value to non-marketed social outcomes. Our research uses cross sectional, farm-level data collected in 2015 from a sample of 180 Catalan arable crop farms specialized in the production of cereals, oilseeds and protein (COP) crops. Our results suggest that our sample farms show high performance scores, from 85% for the bad state of nature to 88% for the normal and ideal crop growing conditions. This suggests that farm performance is increasing with an improvement in crop growth conditions. Results also show that average shadow prices of desirable state-contingent output and social outcomes for efficient and inefficient farms are positive, suggesting that the production of desirable marketable outputs and of non-marketable outputs makes a positive contribution to the farm production efficiency. Results also indicate that social outputs’ shadow prices are contingent upon the growing conditions. The shadow prices follow an upward trend as crop-growing conditions improve. This finding suggests that these efficient farms prefer to allocate more resources in the production of desirable outputs than of social outcomes. To our knowledge, this study represents the first attempt to compute shadow prices of social outcomes while accounting for the stochastic nature of the production technology. Our findings suggest that the decision-making process of the efficient farms in dealing with social issues are stochastic and strongly dependent on the growth conditions. This implies that policy-makers should adjust their instruments according to the stochastic environmental conditions. An optimal redistribution of rural development support, by increasing the public payment with the improvement in crop growth conditions, would likely enhance the effectiveness of public policies.

Keywords: data envelopment analysis, shadow prices, social sustainability, sustainable farming

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Authors: S. Mehrab Amiri, Nasser Talebbeydokhti

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Modeling sediment transport processes by means of numerical approach often poses severe challenges. In this way, a number of techniques have been suggested to solve flow and sediment equations in decoupled, semi-coupled or fully coupled forms. Furthermore, in order to capture flow discontinuities, a number of techniques, like artificial viscosity and shock fitting, have been proposed for solving these equations which are mostly required careful calibration processes. In this research, a numerical scheme for solving shallow water and Exner equations in fully coupled form is presented. First-Order Centered scheme is applied for producing required numerical fluxes and the reconstruction process is carried out toward using Monotonic Upstream Scheme for Conservation Laws to achieve a high order scheme.  In order to satisfy C-property of the scheme in presence of bed topography, Surface Gradient Method is proposed. Combining the presented scheme with fourth order Runge-Kutta algorithm for time integration yields a competent numerical scheme. In addition, to handle non-prismatic channels problems, Cartesian Cut Cell Method is employed. A trained Multi-Layer Perceptron Artificial Neural Network which is of Feed Forward Back Propagation (FFBP) type estimates sediment flow discharge in the model rather than usual empirical formulas. Hydrodynamic part of the model is tested for showing its capability in simulation of flow discontinuities, transcritical flows, wetting/drying conditions and non-prismatic channel flows. In this end, dam-break flow onto a locally non-prismatic converging-diverging channel with initially dry bed conditions is modeled. The morphodynamic part of the model is verified simulating dam break on a dry movable bed and bed level variations in an alluvial junction. The results show that the model is capable in capturing the flow discontinuities, solving wetting/drying problems even in non-prismatic channels and presenting proper results for movable bed situations. It can also be deducted that applying Artificial Neural Network, instead of common empirical formulas for estimating sediment flow discharge, leads to more accurate results.

Keywords: artificial neural network, morphodynamic model, sediment continuity equation, shallow water equations

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Authors: Shamsulhaq Amin

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Drought can arise from several hydrometeorological phenomena that result in insufficient precipitation, soil moisture, and surface and groundwater flow, leading to conditions that are considerably drier than the usual water content or availability. Drought is often assessed using indices that are associated with meteorological, agricultural, and hydrological phenomena. In order to effectively handle drought disasters, it is essential to accurately determine the kind, intensity, and extent of the drought using drought characterization. This information is critical for managing the drought before, during, and after the rehabilitation process. Over a hundred drought assessments have been created in literature to evaluate drought disasters, encompassing a range of factors and variables. Some models utilise solely hydrometeorological drivers, while others employ remote sensing technology, and some incorporate a combination of both. Comprehending the entire notion of drought and taking into account drought indices along with their calculation processes are crucial for researchers in this discipline. Examining several drought metrics in different studies requires additional time and concentration. Hence, it is crucial to conduct a thorough examination of approaches used in drought indices in order to identify the most straightforward approach to avoid any discrepancies in numerous scientific studies. In case of practical application in real-world, categorizing indices relative to their usage in meteorological, agricultural, and hydrological phenomena might help researchers maximize their efficiency. Users have the ability to explore different indexes at the same time, allowing them to compare the convenience of use and evaluate the benefits and drawbacks of each. Moreover, certain indices exhibit interdependence, which enhances comprehension of their connections and assists in making informed decisions about their suitability in various scenarios. This study provides a comprehensive assessment of various drought indices, analysing their types and computation methodologies in a detailed and systematic manner.

Keywords: drought classification, drought severity, drought indices, agricultur, hydrological

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Authors: Iulia E. Falcan

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The transition of the European power sector towards a clean, renewable energy (RE) system faces the challenge of meeting power demand in times of low wind speed and low solar radiation, at a reasonable cost. This is likely to be achieved through a combination of 1) energy storage technologies, 2) development of the cross-border power grid, 3) installed overcapacity of RE and 4) dispatchable power sources – such as biomass. This paper uses NASA; derived hourly data on weather patterns of sixteen European countries for the past twenty-five years, and load data from the European Network of Transmission System Operators-Electricity (ENTSO-E), to develop a stochastic optimization model. This model aims to understand the synergies between the four classes of technologies mentioned above and to determine the optimal configuration of the energy technologies portfolio. While this issue has been addressed before, it was done so using deterministic models that extrapolated historic data on weather patterns and power demand, as well as ignoring the risk of an unbalanced grid-risk stemming from both the supply and the demand side. This paper aims to explicitly account for the inherent uncertainty in the energy system transition. It articulates two levels of uncertainty: a) the inherent uncertainty in future weather patterns and b) the uncertainty of fully meeting power demand. The first level of uncertainty is addressed by developing probability distributions for future weather data and thus expected power output from RE technologies, rather than known future power output. The latter level of uncertainty is operationalized by introducing a Conditional Value at Risk (CVaR) constraint in the portfolio optimization problem. By setting the risk threshold at different levels – 1%, 5% and 10%, important insights are revealed regarding the synergies of the different energy technologies, i.e., the circumstances under which they behave as either complements or substitutes to each other. The paper concludes that allowing for uncertainty in expected power output - rather than extrapolating historic data - paints a more realistic picture and reveals important departures from results of deterministic models. In addition, explicitly acknowledging the risk of an unbalanced grid - and assigning it different thresholds - reveals non-linearity in the cost functions of different technology portfolio configurations. This finding has significant implications for the design of the European energy mix.

Keywords: cross-border grid extension, energy storage technologies, energy system transition, stochastic portfolio optimization

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Authors: Dinesh K. S., D. R. C. V. Jayadevan

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Autism is the most prevalent of a subset of the disorders organized under the umbrella of pervasive developmental disorders. After the publication of Andrew Wakefield's paper in lancet, many critiques deny this connection even without looking in to the matter. The British Medical Journal even put an editorial regarding this issue. BMJ 2010; 340:c1807. But ayurveda has ample of evidences to believe this connectivity. Dysbiosis, yeast growth of the gut, nutritional deficiencies, enzyme deficiencies, essential fatty acid deficiencies, Gastro esophageal reflux disease, indigestion, inflammatory bowel, chronic constipation & its cascade are few of them to note. The purpose of this paper is to present the observed changes in the behavioural symptoms of autistic babies after a gut management protocol which is a usual programme of our autism treatment plan especially after dysbiotic changes after antibiotic administration. Is there any correlation between changes (if significant) in gut symptoms and behavioral problems of autistic babies especially after a dysbiosis induced by antibiotics. Retrospective analysis of the case sheets of autistic patients admitted in Vaidyaratnam P.S.Varier Ayurveda College hospital, kottakkal,kerala, india from September 2010 are taken for the data processing. Autistic patients are used to come to this hospital as a part of their usual course of treatment. We investigated 40 cases diagnosed as autistic by clinical psychologists from different institutions who had dysbiosis induced by antibiotics. Significant change in gut symptoms before and after treatment p<0.05 in most of its components Significant change in behavioral symptoms before and after treatments p<0.05 in most of the components Correlation between gut symptoms change and behavioral symptoms changes after treatment is + 0.86. Conclusion : Selected Polyherbal Ayurveda treatment has significant role to play to make changes abnormal behaviors in autistic babies and has a positive correlation with changes in gut symptoms induced by dysbiosis of antibiotic intake.

Keywords: ayurveda, autism, dysbiosis, antibiotic

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Authors: Riadh Zorgati, Thomas Triboulet

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In quite diverse application areas such as astronomy, medical imaging, geophysics or nondestructive evaluation, many problems related to calibration, fitting or estimation of a large number of input parameters of a model from a small amount of output noisy data, can be cast as inverse problems. Due to noisy data corruption, insufficient data and model errors, most inverse problems are ill-posed in a Hadamard sense, i.e. existence, uniqueness and stability of the solution are not guaranteed. A wide class of inverse problems in physics relates to the Fredholm equation of the first kind. The ill-posedness of such inverse problem results, after discretization, in a very ill-conditioned linear system of equations, the condition number of the associated matrix can typically range from 109 to 1018. This condition number plays the role of an amplifier of uncertainties on data during inversion and then, renders the inverse problem difficult to handle numerically. Similar problems appear in other areas such as numerical optimization when using interior points algorithms for solving linear programs leads to face ill-conditioned systems of linear equations. Devising efficient solution approaches for such system of equations is therefore of great practical interest. Efficient iterative algorithms are proposed for solving a system of linear equations. The approach is based on a preconditioning of the initial matrix of the system with an approximation of a generalized inverse leading to a stochastic preconditioned matrix. This approach, valid for non-negative matrices, is first extended to hermitian, semi-definite positive matrices and then generalized to any complex rectangular matrices. The main results obtained are as follows: 1) We are able to build a generalized inverse of any complex rectangular matrix which satisfies the convergence condition requested in iterative algorithms for solving a system of linear equations. This completes the (short) list of generalized inverse having this property, after Kaczmarz and Cimmino matrices. Theoretical results on both the characterization of the type of generalized inverse obtained and the convergence are derived. 2) Thanks to its properties, this matrix can be efficiently used in different solving schemes as Richardson-Tanabe or preconditioned conjugate gradients. 3) By using Lp norms, we propose generalized Kaczmarz’s type matrices. We also show how Cimmino's matrix can be considered as a particular case consisting in choosing the Euclidian norm in an asymmetrical structure. 4) Regarding numerical results obtained on some pathological well-known test-cases (Hilbert, Nakasaka, …), some of the proposed algorithms are empirically shown to be more efficient on ill-conditioned problems and more robust to error propagation than the known classical techniques we have tested (Gauss, Moore-Penrose inverse, minimum residue, conjugate gradients, Kaczmarz, Cimmino). We end on a very early prospective application of our approach based on stochastic matrices aiming at computing some parameters (such as the extreme values, the mean, the variance, …) of the solution of a linear system prior to its resolution. Such an approach, if it were to be efficient, would be a source of information on the solution of a system of linear equations.

Keywords: conditioning, generalized inverse, linear system, norms, stochastic matrix

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Authors: G. Shahzadi, A. Soulaimani

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This research work aims to identify the physical parameters of the constitutive soil model in the design of a rockfill dam by inverse analysis. The best parameters of the constitutive soil model, are those that minimize the objective function, defined as the difference between the measured and numerical results. The Finite Element code (Plaxis) has been utilized for numerical simulation. Polynomial and neural network-based response surfaces have been generated to analyze the relationship between soil parameters and displacements. The performance of surrogate models has been analyzed and compared by evaluating the root mean square error. A comparative study has been done based on objective functions and optimization techniques. Objective functions are categorized by considering measured data with and without uncertainty in instruments, defined by the least square method, which estimates the norm between the predicted displacements and the measured values. Hydro Quebec provided data sets for the measured values of the Romaine-2 dam. Stochastic optimization, an approach that can overcome local minima, and solve non-convex and non-differentiable problems with ease, is used to obtain an optimum value. Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE) are compared for the minimization problem, although all these techniques take time to converge to an optimum value; however, PSO provided the better convergence and best soil parameters. Overall, parameter identification analysis could be effectively used for the rockfill dam application and has the potential to become a valuable tool for geotechnical engineers for assessing dam performance and dam safety.

Keywords: Rockfill dam, parameter identification, stochastic analysis, regression, PLAXIS

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Authors: Irem Ozten, Neri̇man Caglayan Keles

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Aim: Breastfeeding is a special process for a mother and her baby, and it is the first feeding option for a baby. However, not every society approves of breastfeeding in public to the same degree. The purpose of this study is to determine the attitudes and behaviors of women living in Türkiye toward breastfeeding in public. Materials and methods: This descriptive study was carried out in December 2023 with 515 women (N=515) who had babies aged 0-5 years and breastfed their babies. Based on the review of the literature, an online (Google Forms) data collection form consisting of 40 questions was created. While 13 of these questions were about sociodemographic and obstetric characteristics, 27 were about breastfeeding in public. It took each participant 5-7 minutes to respond to the data collection form by marking their choices on the form. The responses of the participants were analyzed using the R Core Team statistics program. Results: The mean age of the participants (N=515) was 30.6±4.07 (range: 20-44). According to their statements, 76.1% of the participants had undergraduate university degrees, and 77.1% of them had given vaginal birth in their last delivery. While 68.3% of the participants stated that they had heard about the concept of breastfeeding in public, 47.4% said they comfortably breastfed their babies in public, but 33.6% said they breastfed their babies for a shorter period than usual. It was determined that 40% of the participants were embarrassed about being seen by someone while breastfeeding their babies in public, 38.6% were afraid of men while breastfeeding, and 89.7% looked for a suitable place to breastfeed their babies. Among the participants, 93.6% stated that they covered their breasts with a cloth while breastfeeding, 49.5% thought a mother should breastfeed her baby in a place where she can be alone with her baby, and 29.1% thought a mother should breastfeed her baby in private. Conclusion: According to the results of the study, although most women had heard of the concept of breastfeeding in public, and some were comfortable breastfeeding in public, some breastfed their baby in public for a shorter period than usual, they covered their breasts with a cloth while breastfeeding their babies, they were embarrassed about being seen by someone while breastfeeding, and they were afraid of men while breastfeeding. Therefore, awareness should be raised about breastfeeding in public, and environments where mothers can conveniently breastfeed their babies should be created.

Keywords: breastfeeding in public, breastfeeding, breastfeeding attitudes, breastfeeding bahaviors

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Authors: Elham Kiyani

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In this paper, we first introduce the concept of Q-efficient solutions in a real linear space not necessarily endowed with a topology, where Q is some nonempty (not necessarily convex) set. We also used the scalarization technique including the Gerstewitz function generated by a nonconvex set to characterize these Q-efficient solutions. The algebraic concepts of interior and closure are useful to study optimization problems without topology. Studying nonconvex vector optimization is valuable since topological interior is equal to algebraic interior for a convex cone. So, we use the algebraic concepts of interior and closure to define Q-weak efficient solutions and Q-Henig proper efficient solutions of set-valued optimization problems, where Q is not a convex cone. Optimization problems with set-valued maps have a wide range of applications, so it is expected that there will be a useful analytical tool in optimization theory for set-valued maps. These kind of optimization problems are closely related to stochastic programming, control theory, and economic theory. The paper focus on nonconvex problems, the results are obtained by assuming generalized non-convexity assumptions on the data of the problem. In convex problems, main mathematical tools are convex separation theorems, alternative theorems, and algebraic counterparts of some usual topological concepts, while in nonconvex problems, we need a nonconvex separation function. Thus, we consider the Gerstewitz function generated by a general set in a real linear space and re-examine its properties in the more general setting. A useful approach for solving a vector problem is to reduce it to a scalar problem. In general, scalarization means the replacement of a vector optimization problem by a suitable scalar problem which tends to be an optimization problem with a real valued objective function. The Gerstewitz function is well known and widely used in optimization as the basis of the scalarization. The essential properties of the Gerstewitz function, which are well known in the topological framework, are studied by using algebraic counterparts rather than the topological concepts of interior and closure. Therefore, properties of the Gerstewitz function, when it takes values just in a real linear space are studied, and we use it to characterize Q-efficient solutions of vector problems whose image space is not endowed with any particular topology. Therefore, we deal with a constrained vector optimization problem in a real linear space without assuming any topology, and also Q-weak efficient and Q-proper efficient solutions in the senses of Henig are defined. Moreover, by means of the Gerstewitz function, we provide some necessary and sufficient optimality conditions for set-valued vector optimization problems.

Keywords: algebraic interior, Gerstewitz function, vector closure, vector optimization

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Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

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Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

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Authors: Merita Dauti, Edita Alili-Idrizi, Sihana Ahmeti –Lika, Ledjan Malaj

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The extermination and proper disposal of pharmaceutical wastes from expired and unused medications remains a disputable issue due to their specific nature and characteristics. Even though the hazards from these wastes are already well known in terms of environment and human health, people still treat them as usual wastes. At a national level, in many countries the management of pharmaceutical and medical wastes has been one of the main objectives in order to protect people’s health and the environment. Even though many legal regulations exist in this respect, there has not been a single law that would clearly explain the procedures of returning medicines, ways of selection, treatment and extermination of pharmaceutical wastes. This paper aims at analyzing the practices of pharmaceutical waste management and treatment in some European countries as well as a review of the legislation and official guidelines in managing these kinds of wastes and protecting the environment and human health. A suitable treatment and management of expired medications and other similar wastes would be in the interest of public health in the first place, as well as in the interest of healthcare institutions and other bodies engaged in environment protection.

Keywords: pharmaceutical waste, legal regulation, proper disposal, environment pollution

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Authors: Shabnam Pashaei, Mohammadali Badamchizadeh

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This paper presents a new fractional-order terminal sliding mode control for synchronization of two different fractional-order chaotic systems with uncertainty and external disturbances. A fractional-order integral type nonlinear switching surface is presented. Then, using the Lyapunov stability theory and sliding mode theory, a fractional-order control law is designed to synchronize two different fractional-order chaotic systems. Finally, a simulation example is presented to illustrate the performance and applicability of the proposed method. Based on numerical results, the proposed controller ensures that the states of the controlled fractional-order chaotic response system are asymptotically synchronized with the states of the drive system.

Keywords: terminal sliding mode control, fractional-order calculus, chaotic systems, synchronization

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Authors: Yousif Mohamed Y. Abdallah, Eltayeb Wagallah

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Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. Image enhancement is the transformation or mapping of one image to another. The enhancement of certain features in images is accompanied by undesirable effects. To achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian scale mixture model and median filter were presented, which accomplishes nonlinearities from scattering a new nonlinear approach for contrast enhancement of bones in bone scan images using both gamma correction and negative transform methods. The usual assumption of a distribution of gamma and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity but to underestimation in regions of high intensity and therefore to non-optional results. The contrast enhancement results were obtained and evaluated using MatLab program in nuclear medicine images of the bones. The optimal number of bins, in particular the number of gray-levels, is chosen automatically using entropy and average distance between the histogram of the original gray-level distribution and the contrast enhancement function’s curve.

Keywords: bone scan, nuclear medicine, Matlab, image processing technique

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Authors: Prayas Sharma

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This paper proposed a generalized class of estimators, an exponential class of estimators based on the adaption of Sharma and Singh (2015) and Solanki and Singh (2013), and a simple difference estimator for estimating unknown population mean in the case of Poisson distributed population in simple random sampling without replacement. The expressions for mean square errors of the proposed classes of estimators are derived from the first order of approximation. It is shown that the adapted version of Solanki and Singh (2013), the exponential class of estimator, is always more efficient than the usual estimator, ratio, product, exponential ratio, and exponential product type estimators and equally efficient to simple difference estimator. Moreover, the adapted version of Sharma and Singh's (2015) estimator is always more efficient than all the estimators available in the literature. In addition, theoretical findings are supported by an empirical study to show the superiority of the constructed estimators over others with an application to earthquake data of Turkey.

Keywords: auxiliary attribute, point bi-serial, mean square error, simple random sampling, Poisson distribution

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Authors: Salima Kouici, Abdelkader Khelladi

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In this work, we begin with the presentation of the Tθ family of usual similarity measures concerning multidimensional binary data. Subsequently, some properties of these measures are proposed. Finally, the impact of the use of different inter-elements measures on the results of the Agglomerative Hierarchical Clustering Methods is studied.

Keywords: binary data, similarity measure, Tθ measures, agglomerative hierarchical clustering

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Authors: Mohammad Ebrahim Arbabian

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This paper investigates the impact of 3D printing, also known as additive manufacturing, on a multi-item supply chain comprising a manufacturer and retailer. Operating under a wholesale-price contract and catering to stochastic customer demand, this study delves into the largely unexplored realm of how 3D printing technology reshapes supply chain dynamics. A distinguishing aspect of 3D printing is its versatility in producing various product types, yet its slower production pace compared to traditional methods poses a challenge. We analyze the trade-off between 3D printing's limited capacity and its enhancement of production flexibility. By delineating the economic circumstances favoring 3D printing adoption by the manufacturer, we establish the Stackelberg equilibrium in the retailer-manufacturer game. Additionally, we determine optimal order quantities for the retailer considering 3D printing as an option for the manufacturer, ascertain optimal wholesale prices in the presence of 3D printing, and compute optimal profits for both parties involved in the supply chain.

Keywords: additive manufacturing, supply chain management, contract theory, Stackelberg game, optimization

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Authors: Y. Wang, S. Zhang, X. Chen

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The longitudinal compressive strength of fibre reinforced plastic (FRP) possess a large stochastic variability, which limits efficient application of composite structures. This study aims to address how the random fibre packing affects the uncertainty of FRP compressive strength. An novel approach is proposed to generate random fibre packing status by a combination of Latin hypercube sampling and random sequential expansion. 3D nonlinear finite element model is built which incorporates both the matrix plasticity and fibre geometrical instability. The matrix is modeled by isotropic ideal elasto-plastic solid elements, and the fibres are modeled by linear-elastic rebar elements. Composite with a series of different nominal fibre volume fractions are studied. Premature fibre waviness at different magnitude and direction is introduced in the finite element model. Compressive tests on uni-directional CFRP (carbon fibre reinforced plastic) are conducted following the ASTM D6641. By a comparison of 3D FE models and compressive tests, it is clearly shown that the stochastic variation of compressive strength is partly caused by the random fibre packing, and normal or lognormal distribution tends to be a good fit the probabilistic compressive strength. Furthermore, it is also observed that different random fibre packing could trigger two different fibre micro-buckling modes while subjected to longitudinal compression: out-of-plane buckling and twisted buckling. The out-of-plane buckling mode results much larger compressive strength, and this is the major reason why the random fibre packing results a large uncertainty in the FRP compressive strength. This study would contribute to new approaches to the quality control of FRP considering higher compressive strength or lower uncertainty.

Keywords: compressive strength, FRP, micro-buckling, random fibre packing

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Authors: Christian Rathgeber, Franz Winkler, Xiaoyu Kang, Steffen Müller

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In this contribution two approaches for calculating optimal trajectories for highly automated vehicles are presented and compared. The first one is based on a non-linear vehicle model, used for evaluation. The second one is based on a simplified model and can be implemented on a current ECU. In usual driving situations both approaches show very similar results.

Keywords: trajectory planning, direct method, indirect method, highly automated driving

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Authors: Hossein Jafari, Xiangfang Li, Lijun Qian, Alexander Aved, Timothy Kroecker

Abstract:

Traditionally in sensor networks and recently in the Internet of Things, numerous heterogeneous sensors are deployed in distributed manner to monitor a phenomenon that often can be model by an underlying stochastic process. The big time-series data collected by the sensors must be analyzed to detect change in the stochastic process as quickly as possible with tolerable false alarm rate. However, sensors may have different accuracy and sensitivity range, and they decay along time. As a result, the big time-series data collected by the sensors will contain uncertainties and sometimes they are conflicting. In this study, we present a framework to take advantage of Evidence Theory (a.k.a. Dempster-Shafer and Dezert-Smarandache Theories) capabilities of representing and managing uncertainty and conflict to fast change detection and effectively deal with complementary hypotheses. Specifically, Kullback-Leibler divergence is used as the similarity metric to calculate the distances between the estimated current distribution with the pre- and post-change distributions. Then mass functions are calculated and related combination rules are applied to combine the mass values among all sensors. Furthermore, we applied the method to estimate the minimum number of sensors needed to combine, so computational efficiency could be improved. Cumulative sum test is then applied on the ratio of pignistic probability to detect and declare the change for decision making purpose. Simulation results using both synthetic data and real data from experimental setup demonstrate the effectiveness of the presented schemes.

Keywords: CUSUM, evidence theory, kl divergence, quickest change detection, time series data

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Authors: Iacopo Carnacina, Mohammad Keshtpoor, Richard Yablonsky

Abstract:

Non-structural protection measures against flooding are becoming increasingly popular flood risk mitigation strategies. In particular, coastal flood insurance impacts not only private citizens but also insurance and reinsurance companies, who may require it to retain solvency and better understand the risks they face from a catastrophic coastal flood event. In this context, a framework is presented here to assess the risk for coastal flooding across the UK. The area has a long history of catastrophic flood events, including the Great Flood of 1953 and the 2013 Cyclone Xaver storm, both of which led to significant loss of life and property. The current framework will leverage a technology based on a hydrodynamic model (Delft3D Flexible Mesh). This flexible mesh technology, coupled with a calibration technique, allows for better utilisation of computational resources, leading to higher resolution and more detailed results. The generation of a stochastic set of extra tropical cyclone (ETC) events supports the evaluation of the financial losses for the whole area, also accounting for correlations between different locations in different scenarios. Finally, the solution shows a detailed analysis for the Thames River, leveraging the information available on flood barriers and levees. Two realistic disaster scenarios for the Greater London area are simulated: In the first scenario, the storm surge intensity is not high enough to fail London’s flood defences, but in the second scenario, London’s flood defences fail, highlighting the potential losses from a catastrophic coastal flood event.

Keywords: storm surge, stochastic model, levee failure, Thames River

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Authors: Lavinia Flueckiger, Roselind Lieb, Andrea H. Meyer, Cornelia Witthauer, Jutta Mata

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Objective: Health behaviors tend to show a high variability over time within the same person. However, most existing research can only assess a snapshot of a person’s behavior and not capture this natural daily variability. Two intensive longitudinal studies examine the variability in health behavior over one academic year and their implications for other aspects of daily life such as affect and academic performance. Can already a single day of increased physical activity, snacking, or improved sleep have beneficial effects? Methods: In two intensive longitudinal studies with up to 65 assessment days over an entire academic year, university students (Study 1: N = 292; Study 2: N = 304) reported sleep quality, physical activity, snacking, positive and negative affect, and learning goal achievement. Results: Multilevel structural equation models showed that on days on which participants reported better sleep quality or more physical activity than usual, they also reported increased positive affect, decreased negative affect, and better learning goal achievement. Higher day-to-day snacking was only associated with increased positive affect. Both, increased day-to-day sleep quality and physical activity were indirectly associated with better learning goal achievement through changes in positive and negative affect; results for snacking were mixed. Importantly, day-to-day sleep quality was a stronger predictor for affect and learning goal achievement than physical activity or snacking. Conclusion: One day of better sleep or more physical activity than usual is associated with improved affect and academic performance. These findings have important implications for low-threshold interventions targeting the improvement of daily functioning.

Keywords: sleep quality, physical activity, snacking, affect, academic performance, multilevel structural equation model

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Authors: Cita Ayeni

Abstract:

Following countless violations of Human Rights in Nigeria by the various arms and agencies of government; from the Military to the Federal Police and other law enforcement agencies, Nigeria has been riddled with several reports of acts by these agencies against the citizens, ranging from illegal arrest and imprisonment, torture, disappearing, and extrajudicial killings, just to mention a few. This paper, focuses on SARS (Special Anti-Robbery Squad), a division of the Nigeria Police Force, and its reported threats to the people’s security, particularly the Nigerian youths, with continuous violence, extortion, illegal arrest and imprisonment, terror, and extrajudicial activities resulting in maiming and in most cases death, thus infringing on the human rights of the people it’s sworn to protect. This research further analyses how the activities of SARS has over the years instigated fear on the average Nigerian youth, preventing the free participation in daily life, education, job, and individual development, in turn impeding the realization of their full potentials for growth and participation in collective national development. This research analyzes the executive order by the then Acting President (Vice-President) of Nigeria, directing the overhauling of SARS, and its implementation by the Federal Police Force in determining if it’s enough to prevent or put a stop to the continuous Human Rights abuse and threat to the security of the individual citizen. Concluding that although the order by the Acting President was given with an intent to halt the various violations by SARS, and the Inspector General of Police’s (IGP) subsequent action by releasing a statement following the order, the bureaucracy in Nigeria, with a history of incompetency and a return to 'business as usual' after a reduced public outcry, it’s most likely that there will not be adequate follow up put in place and these violations would be slowly 'swept under the rug' with SARS officials not held accountable. It is recommended therefore that the Federal Government through the NPF, following the reforms made, in collaboration with the mentioned Independent Human Rights and civil societies organizations should periodically produce unbiased and publicly accessible reports on the implementation of these reforms and progress made. This will go a long way in assuring the public of actual fulfillment of the restructuring, reduce fear by the youths and restore some public faith in the government.

Keywords: special anti-robbery squad, youths in Nigeria, overhaul, insecurities, human rights violations

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Authors: Nancy Sakr, Mohamed Abou-Zeid

Abstract:

World energy consumption has increased rapidly in the past few years. Due to population growth, total energy consumption is increasing; a large amount of energy is wasted on the cooling and heating processes in buildings. However, using thermal heating management can minimize costs, heat consumption and create a management system for the heat insulation for buildings. This concept is being implemented through different approaches. Based on analysis and research, there is evidence in the energy consumption before and after testing and applying construction approaches for thermal heating management in building units. This investigation addresses the evaluation of the influence of external coatings on energy consumption. Coatings are considered one of the smart effective available approaches for energy efficiency. Unfortunately, this approach is not widely applied in the construction industry. It needs more data to prove effectiveness and credibility between people to use it as a smart thermal insulation approach. Two precedents have been analyzed in order to monitor buildings’ heat exposure, and how the buildings will be affected by thermal insulation materials. Data sheets from chemical companies which produce similar coatings are compared with the usual products and the protective thermal products.

Keywords: energy consumption, building envelope, thermal insulation, protective coatings

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Authors: Hasan Keshavarzian, Tayebeh Nesari

Abstract:

Bolted rail joint is one of the most vulnerable areas in railway track. A comprehensive approach was developed for studying the reliability of fatigue crack initiation of railroad bolt hole under random axle loads and random material properties. The operation condition was also considered as stochastic variables. In order to obtain the comprehensive probability model of fatigue crack initiation life prediction in railroad bolt hole, we used FEM, response surface method (RSM), and reliability analysis. Combined energy-density based and critical plane based fatigue concept is used for the fatigue crack prediction. The dynamic loads were calculated according to the axle load, speed, and track properties. The results show that axle load is most sensitive parameter compared to Poisson’s ratio in fatigue crack initiation life. Also, the reliability index decreases slowly due to high cycle fatigue regime in this area.

Keywords: rail-wheel tribology, rolling contact mechanic, finite element modeling, reliability analysis

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Authors: Maria C. Almario, Pam Remer, Jeff Resse, Kathy Moran, Linda Theander Adam

Abstract:

The identification of victims of human trafficking and consequential service provision is characterized by a significant disconnection between the estimated prevalence of this issue and the number of cases identified. This poses as tremendous problem for human rights advocates as it prevents data collection, information sharing, allocation of resources and opportunities for international dialogues. The current paper introduces the Inclusive Human Trafficking Checklist (IHTC) as a measurement methodology with theoretical underpinnings derived from dialectic theory. The presence of human trafficking in a person’s life is conceptualized as a dynamic and dialectic interaction between vulnerability and exploitation. The current papers explores the operationalization of exploitation and vulnerability, evaluates the metric qualities of the instrument, evaluates whether there are differences in assessment based on the participant’s profession, level of knowledge, and training, and assesses if users of the instrument perceive it as useful. A total of 201 participants were asked to rate three vignettes predetermined by experts to qualify as a either human trafficking case or not. The participants were placed in three conditions: business as usual, utilization of the IHTC with and without training. The results revealed a statistically significant level of agreement between the expert’s diagnostic and the application of the IHTC with an improvement of 40% on identification when compared with the business as usual condition While there was an improvement in identification in the group with training, the difference was found to have a small effect size. Participants who utilized the IHTC showed an increased ability to identify elements of identity-based vulnerabilities as well as elements of fraud, which according to the results, are distinctive variables in cases of human trafficking. In terms of the perceived utility, the results revealed higher mean scores for the groups utilizing the IHTC when compared to the business as usual condition. These findings suggest that the IHTC improves appropriate identification of cases and that it is perceived as a useful instrument. The application of the IHTC as a multidisciplinary instrumentation that can be utilized in legal and human services settings is discussed as a pivotal piece of helping victims restore their sense of dignity, and advocate for legal, physical and psychological reparations. It is noteworthy that this study was conducted with a sample in the United States and later re-tested in Colombia. The implications of the instrument for treatment conceptualization and intervention in human trafficking cases are discussed as opportunities for enhancement of victim well-being, restoration engagement and activism. With the idea that what is personal is also political, we believe that the careful observation and data collection in specific cases can inform new areas of human rights activism.

Keywords: exploitation, human trafficking, measurement, vulnerability, screening

Procedia PDF Downloads 311